Recently, some learning-based lossy image compression approaches have been proposed to achieve competitive performance [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}. They all utilize auto-encoder style networks to transform pixels to quantized latent representations and then project back to the pixel space. Indeed, a jointly optimized entropy model is verified to make the latent presentations more suitable to be losslessly compressed to a bitstream based on entropy coding. Some works [4]}, [2]}, [10]} try to improve the representation ability of the auto-encoder architecture. Ballé et al. [4]} bring in the generalized divisive normalization (GDN) transform with optimized parameters to efficiently Gaussianize the local joint statistics of natural images. Toderci et al. [2]} introduce a new gated recurrent unit (GRU) inspired by the residual network. Cheng et al. [9]} introduce deep residual attention modules to improve the performance.
